Sepsis

Cross-LLM AI platform meta-research: Non-inferiority of bovine milk-based fortifiers to human milk-based fortifiers

Necrotizing enterocolitis (NEC), frequently resulting in sepsis, is among the leading causes of morbidity and mortality of pre-term newborns. However, diagnostic and therapeutic strategies for NEC and sepsis are still limited and controversial. In this context, there are ongoing debates regarding the application of human milk-based fortifiers (HMF) versus bovine milk-based fortifiers (BMF), but ro...

ADVISE: A Machine Learning Framework for Early Recognition of a Surrogate Marker for Ventilator-Associated Pneumonia Using Routinely Collected Critical Care Data

BackgroundVentilator-associated pneumonia (VAP) is the most frequent nosocomial infection in critical care, affecting 20-36% of mechanically ventilated patients. Early prediction is hampered by the absence of a reliable, objective diagnostic standard. We developed ADVISE (Automated Dudley Ventilation Infection Series Evaluation), a machine learning model to predict physiological deterioration cons...

Label-free Pathogen Identification with Microscopy Imaging and Deep Learning

Rapid and accurate pathogen identification is crucial for the clinical management of infectious diseases, particularly sepsis and severe respiratory i...

Real-world results from a Machine Learning-guided, phenotypic High-Throughput Screen for novel antibiotics

Antimicrobial resistance is an urgent global health threat, with over 2.8 million multidrug-resistant infections killing over 35,000 annually in the U...

Reduced dopaminergic reinforcement, not learning capacity, limits operant learning in aging Drosophila

Aging is associated with a progressive decline in cognitive function, including the ability to adapt behavior based on its consequences. While classic...

Vancomycert: A Certified Neuro-Symbolic Drug Delivery System (Case Study)

Neural network controllers for autonomous decision-making are well-established in cyber-physical systems, yet their deployment in safety-critical heal...

Predicting Mouse Lifespan-Extending Chemical Compounds with Machine Learning

Pharmacological interventions targeting the biological processes of ageing hold significant potential to extend healthspan and promote longevity. This...

AURA: Active-Response Attribution under Treatment Ambiguity in Bacterial Cytological Profiling

When a bacterial sample is exposed to several antibiotics, not every applied drug necessarily acts: if the organism is resistant to one of them, that ...

Virus-human protein-protein interactions predict viral phenotypes

Viral phenotypes such as host and tissue tropism are critical determinants of viral infection and transmission. Inferring viral phenotypes presents un...

Small molecule biomarkers predictive of Chagas disease progression

Chagas disease (CD), caused by the protozoan parasite Trypanosoma cruzi, affects an estimated 10.5 million people worldwide and remains a leading caus...

A Deep Hypergraph Learning Model for Predicting Antimicrobial Combination Effects Across Bacterial Targets

Antimicrobial resistance (AMR) creates an urgent need for efficient strategies to identify effective antibacterial combinations. Combination therapy, ...

Machine Learning-Guided Discovery of Bacterial-Selective Membrane-Active Compounds Reveals Mechanistic Bias in Antibiotic Training Datasets

The rise of antibiotic resistance necessitates the discovery of antibacterial compounds with novel mechanisms of action (MoAs). Recent machine learnin...

Pairing Data Independent Acquisition and High-Resolution Full Scan for Fast Urinary Tract Infection Diagnosis

Background: Rapid and accurate identification of urinary tract infection (UTI) pathogens is critical for effective treatment and combating antimicrobi...

BacteReason: A Reasoning Model for Antimicrobial Resistance Prediction

The rapid global spread of antimicrobial resistance (AMR) has placed unprecedented pressure on clinical decision-making. Machine learning predictors o...

Calibrated and Interpretable Machine Learning for ICU Mortality Prediction Using First 24-Hour Clinical Data

Objective: To develop, calibrate, and interpret machine learning models for predicting in-hospital mortality among intensive care unit (ICU) patients ...

Conformal Prediction and Ensemble Learning for Uncertainty-Aware ICU Mortality Stratification

Background. Conventional ICU severity scores - SOFA, qSOFA, and APACHE-II - use additive integer weightings that cannot capture non-linear organ failu...

Real-world impact of a sepsis early detection model integrated into clinical workflow: a quasi-experimental study

Background: Sepsis is a life-threatening condition in which delayed recognition and treatment are associated with increased mortality. While predictiv...

High-dimensional Characterization of Genome-Environment Fitness Landscapes in Klebsiella pneumoniae

Background Bacterial fitness is shaped by interactions between genome variation and environmental context, yet how these interactions determine its pr...

Development and validation of a dynamic risk stratification tool for predicting multidrug-resistant bacterial infections in ICU patients: A clinical prediction model and web-based calculator

Background: Multi-drug resistant Bacterial (MDRB) Infections in the intensive care units (ICUs) substantially elevate patient mortality, prolong hospi...

Using Disinhibition versus Direct Control in a Spiking Neural Model of Dopamine-Driven Reinforcement Learning

Dopaminergic signalling is central to value learning and decision making. It has been observed that multiple pathways with different patterns of conne...